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Journal of the Optical Society of America A

Journal of the Optical Society of America A


  • Editor: Franco Gori
  • Vol. 28, Iss. 6 — Jun. 1, 2011
  • pp: 1145–1163

Channelized Hotelling observers for the assessment of volumetric imaging data sets

Ljiljana Platiša, Bart Goossens, Ewout Vansteenkiste, Subok Park, Brandon D. Gallas, Aldo Badano, and Wilfried Philips  »View Author Affiliations

JOSA A, Vol. 28, Issue 6, pp. 1145-1163 (2011)

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Current clinical practice is rapidly moving in the direction of volumetric imaging. For two-dimensional (2D) images, task-based medical image quality is often assessed using numerical model observers. For three- dimensional (3D) images, however, these models have been little explored so far. In this work, first, two novel designs of a multislice channelized Hotelling observer (CHO) are proposed for the task of detecting 3D signals in 3D images. The novel designs are then compared and evaluated in a simulation study with five different CHO designs: a single-slice model, three multislice models, and a volumetric model. Four different random background statistics are considered, both Gaussian (noncorrelated and correlated Gaussian noise) and non-Gaussian (lumpy and clustered lumpy backgrounds). Overall, the results show that the volumetric model outperforms the others, while the disparity between the models decreases for greater complexity of the detection task. Among the multislice models, the second proposed CHO could most closely approach the volumetric model, whereas the first new CHO seems to be least affected by the number of training samples.

© 2011 Optical Society of America

OCIS Codes
(110.2960) Imaging systems : Image analysis
(110.2970) Imaging systems : Image detection systems
(110.3000) Imaging systems : Image quality assessment
(330.1880) Vision, color, and visual optics : Detection
(330.5510) Vision, color, and visual optics : Psychophysics
(110.4155) Imaging systems : Multiframe image processing

ToC Category:
Imaging Systems

Original Manuscript: March 16, 2010
Revised Manuscript: March 1, 2011
Manuscript Accepted: March 4, 2011
Published: May 20, 2011

Virtual Issues
Vol. 6, Iss. 7 Virtual Journal for Biomedical Optics

Ljiljana Platiša, Bart Goossens, Ewout Vansteenkiste, Subok Park, Brandon D. Gallas, Aldo Badano, and Wilfried Philips, "Channelized Hotelling observers for the assessment of volumetric imaging data sets," J. Opt. Soc. Am. A 28, 1145-1163 (2011)

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  1. American Association of Physicists in Medicine, “Specification and acceptance testing of computed tomography scanners,” Tech. Rep. 39 (American Association of Physicists in Medicine, 1993).
  2. National Electrical Manufacturers Association (NEMA), “Performance measurements of positron emission tomographs,” NEMA NU 2-2007 (NEMA, 2007).
  3. M. A. Lodge, A. Rahmim, and R. L. Wahl, “A practical, automated quality assurance method for measuring spatial resolution in pet,” J. Nucl. Med. 50, 1307–1314 (2009). [CrossRef] [PubMed]
  4. P. F. Judy, R. G. Swensson, and M. Szulc, “Lesion detection and signal-to-noise ratio in CT images,” Med. Phys. 8, 13–23 (1981). [CrossRef] [PubMed]
  5. K. J. Myers, H. H. Barrett, M. C. Borgstrom, E. B. Cargill, A. V. Clough, R. D. Fiete, T. D. Milster, D. D. Patton, R. G. Paxman, G. W. Seeley, W. E. Smith, and M. O. Stempski, “A systematic approach to the design of diagnostic systems for nuclear medicine,” in Information Processing in Medical Imaging: Proceedings of the Ninth Conference, S.L.Bacharach, ed. (Martinus Nijhoff, 1986), pp. 431–444.
  6. H. H. Barrett, “Objective assessment of image quality: effects of quantum noise and object variability,” J. Opt. Soc. Am. A 7, 1266–1278 (1990). [CrossRef] [PubMed]
  7. H. H. Barrett and K. J. Myers, Foundations of Image Science (Wiley, 2004).
  8. B. I. Reiner, E. L. Siegel, F. J. Hooper, S. Pomerantz, A. Dahlke, and D. Rallis, “Radiologists’ productivity in the interpretation of CT scans: a comparison of PACS with conventional film,” Am. J. Roentgenol. 176, 861–864 (2001).
  9. A. Rahmim and H. Zaidi, “PET versus SPECT: strengths, limitations and challenges,” Nucl. Med. Commun. 29, 193–207 (2008). [CrossRef] [PubMed]
  10. I. Andersson, D. M. Ikeda, S. Zackrisson, M. Ruschin, T. Svahn, P. Timberg, and A. Tingberg, “Breast tomosynthesis and digital mammography: a comparison of breast cancer visibility and birads classification in a population of cancers with subtle mammographic findings,” Eur. Radiol. 18, 2817–2825 (2008). [CrossRef] [PubMed]
  11. H. H. Barrett, J. Yao, J. P. Rolland, and K. J. Myers, “Model observers for assessment of image quality,” Proc. Natl. Acad. Sci. USA 90, 9758–9765 (1993). [CrossRef] [PubMed]
  12. H. H. Barrett, J. L. Denny, R. F. Wagner, and K. J. Myers, “Objective assessment of image quality. II. Fisher information, Fourier crosstalk, and figures of merit for task performance,” J. Opt. Soc. Am. A 12, 834–852 (1995). [CrossRef]
  13. S. Park, M. A. Kupinski, E. Clarkson, and H. H. Barrett, “Ideal-observer performance under signal and background uncertainty,” in Information Processing in Medical Imaging, C.J.Taylor and J.A.Noble, eds., Lecture Notes in Computer Science (Springer, 2003), Vol.  2732, pp. 342–353. [CrossRef]
  14. M. A. Kupinski, J. W. Hoppin, E. Clarkson, and H. H. Barrett, “Ideal-observer computation in medical imaging with use of Markov-chain Monte Carlo techniques,” J. Opt. Soc. Am. A 20, 430–438 (2003). [CrossRef]
  15. B. D. Gallas and H. H. Barrett, “Validating the use of channels to estimate the ideal linear observer,” J. Opt. Soc. Am. A 20, 1725–1738 (2003). [CrossRef]
  16. M. P. Eckstein, C. K. Abbey, and J. S. Whiting, “Human vs. model observers in anatomic backgrounds,” Proc. SPIE 3340, 16–26 (1998). [CrossRef]
  17. C. K. Abbey and H. H. Barrett, “Human- and model-observer performance in ramp-spectrum noise: effects of regularization and object variability,” J. Opt. Soc. Am. A 18, 473–488 (2001). [CrossRef]
  18. J. A. Swets and R. M. Pickett, Evaluation of Diagnostic Systems: Methods from Signal Detection Theory (Academic, 1982).
  19. C. E. Metz, “Quantification of failure to demonstrate statistical significance. The usefulness of confidence intervals,” Invest. Radiol. 28, 59–63 (1993). [CrossRef] [PubMed]
  20. H. H. Barrett, C. K. Abbey, and E. Clarkson, “Objective assessment of image quality. III. ROC metrics, ideal observers, and likelihood-generating functions,” J. Opt. Soc. Am. A 15, 1520–1535 (1998). [CrossRef]
  21. D. M. Green and J. A. Swets, Signal Detection Theory and Psychophysics (Krieger, 1974).
  22. K. J. Myers and H. H. Barrett, “Addition of a channel mechanism to the ideal-observer model,” J. Opt. Soc. Am. A 4, 2447–2457(1987). [CrossRef] [PubMed]
  23. H. H. Barrett, K. J. Myers, B. D. Gallas, E. Clarkson, and H. Zhang, “Megalopinakophobia: its symptoms and cures,” Proc. SPIE 4320, 299–307 (2001). [CrossRef]
  24. S. Park, J. M. Witten, and K. J. Myers, “Singular vectors of a linear imaging system as efficient channels for the Bayesian ideal observer,” IEEE Trans. Med. Imag. 28, 657–668(2009). [CrossRef]
  25. S. Park and E. Clarkson, “Efficient estimation of ideal-observer performance in classification tasks involving high-dimensional complex backgrounds,” J. Opt. Soc. Am. A 26, B59–B71(2009). [CrossRef]
  26. J. M. Witten, S. Park, and K. J. Myers, “Partial least squares: a method to estimate efficient channels for the ideal observers,” IEEE Trans. Med. Imag. 29, 1050–1058 (2010). [CrossRef]
  27. S. Park, E. Clarkson, H. H. Barrett, M. A. Kupinski, and K. J. Myers, “Performance of a channelized-ideal observer using Laguerre-Gauss channels for detecting a Gaussian signal at a known location in different lumpy backgrounds,” Proc. SPIE 6146, 61460P (2006). [CrossRef]
  28. S. Park, H. H. Barrett, E. Clarkson, M. A. Kupinski, and K. J. Myers, “Channelized-ideal observer using Laguerre-Gauss channels in detection tasks involving non-Gaussian distributed lumpy backgrounds and a Gaussian signal,” J. Opt. Soc. Am. A 24, B136–B150 (2007). [CrossRef]
  29. K. Myers, H. Barrett, M. Borgstrom, D. Patton, and G. Seeley, “Effect of noise correlation on detectability of disk signals in medical imaging,” J. Opt. Soc. Am. A 2, 1752–1759(1985). [CrossRef] [PubMed]
  30. J. S. Kim, P. Kinahan, C. Lartizien, C. Comtat, and T. Lewellen, “A comparison of planar versus volumetric numerical observers for detection task performance in whole-body PET imaging,” IEEE Trans. Nucl. Sci. 51, 34–40 (2004). [CrossRef]
  31. J. P. Rolland and H. H. Barrett, “Effect of random background inhomogeneity on observer detection performance,” J. Opt. Soc. Am. A 9, 649–658 (1992). [CrossRef] [PubMed]
  32. A. E. Burgess, F. L. Jacobson, and P. F. Judy, “Human observer detection experiments with mammograms and power-law noise,” Med. Phys. 28, 419–437 (2001). [CrossRef] [PubMed]
  33. S. Park, B. D. Gallas, A. Badano, N. A. Petrick, and K. J. Myers, “Efficiency of the human observer for detecting a Gaussian signal at a known location in non-Gaussian distributed lumpy backgrounds,” J. Opt. Soc. Am. A 24, 911–921 (2007). [CrossRef]
  34. M. Chen, J. Bowsher, A. Baydush, K. Gilland, D. DeLong, and R. Jaszczak, “Using the hotelling observer on multislice and multiview simulated SPECT myocardial images,” IEEE Trans. Nucl. Sci. 49, 661–667 (2002). [CrossRef]
  35. C. Lartizien, P. E. Kinahan, and C. Comtat, “Volumetric model and human observer comparisons of tumor detection for whole-body positron emission tomography,” Acad. Radiol. 11, 637–648 (2004). [CrossRef] [PubMed]
  36. S. Young, S. Park, S. K. Anderson, A. Badano, K. J. Myers, and P. Bakic, “Estimating breast tomosynthesis performance in detection tasks with variable-background phantoms,” Proc. SPIE 7258, 72580O (2009). [CrossRef]
  37. S. Park, A. Badano, B. Gallas, and K. Myers, “Incorporating human contrast sensitivity in model observers for detection tasks,” IEEE Trans. Med. Imag. 28, 339–347 (2009). [CrossRef]
  38. H. Gifford, M. King, P. Pretorius, and R. Wells, “A comparison of human and model observers in multislice LROC studies,” IEEE Trans. Med. Imag. 24, 160–169 (2005). [CrossRef]
  39. S. Park, E. Clarkson, M. A. Kupinski, and H. H. Barrett, “Efficiency of the human observer detecting random signals in random backgrounds,” J. Opt. Soc. Am. A 22, 3–16 (2005). [CrossRef]
  40. C. Castella, M. P. Eckstein, C. K. Abbey, K. Kinkel, F. R. Verdun, R. S. Saunders, E. Samei, and F. O. Bochud, “Mass detection on mammograms: influence of signal shape uncertainty on human and model observers,” J. Opt. Soc. Am. A 26, 425–436 (2009). [CrossRef]
  41. H. Liang, S. Park, B. D. Gallas, K. J. Myers, and A. Badano, “Image browsing in slow medical liquid crystal displays,” Acad. Radiol. 15, 370–382 (2008). [CrossRef] [PubMed]
  42. F. O. Bochud, C. K. Abbey, and M. P. Eckstein, “Statistical texture synthesis of mammographic images with clustered lumpy backgrounds,” Opt. Express 4, 33–43 (1999). [CrossRef] [PubMed]
  43. C. Castella, K. Kinkel, F. Descombes, M. P. Eckstein, P.-E. Sottas, F. R. Verdun, and F. O. Bochud, “Mammographic texture synthesis: second-generation clustered lumpy backgrounds using agenetic algorithm,” Opt. Express 16, 7595–7607 (2008). [CrossRef] [PubMed]
  44. E. Clarkson, M. A. Kupinski, and H. H. Barrett, “A probabilistic development of the MRMC method,” Acad. Radiol. 13, 1410–1421 (2006). [CrossRef] [PubMed]
  45. B. D. Gallas, “One-shot estimate of MRMC variance: AUC,” Acad. Radiol. 13, 353–362 (2006). [CrossRef] [PubMed]
  46. L. Platisa, B. Goossens, E. Vansteenkiste, A. Badano, and W. Philips, “Channelized hotelling observers for the detection of 2D signals in 3D simulated images,” in ICIP ’09 Proceedings of the 16th IEEE International Conference on Image Processing (IEEE, 2009), pp. 1781–1784.
  47. R. Wells, M. King, H. Gifford, and P. Pretorius, “Single-slice versus multi-slice display for human-observer lesion-detection studies,” IEEE Trans. Nucl. Sci. 47, 1037–1044(2000). [CrossRef]
  48. K. Fukunaga and R. R. Hayes, “Effects of sample size in classifier design,” IEEE Trans. Pattern Anal. Mach. Intell. 11, 873–885(1989). [CrossRef]
  49. C. J. van den Branden Lambrecht, “A working spatio-temporal model of the human visual system for image restoration and quality assessment applications,” in 1996 Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (IEEE, 1996), pp. 2291–2294. [CrossRef]

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